Intelligent blending system

ABSTRACT

A blending system is provided for contextual blending. The blending system may include an ingredient component and a recipe component. The ingredient component may identify ingredients and determine a quantity of each type of ingredient. The recipe component may create a recipe based on the identified ingredients and the determined quantities of the ingredients. The recipe may include instructions for controlling a blender device.

TECHNICAL FIELD

The present invention relates to a blending system and, moreparticularly, to an intelligent blending system that generates anintelligent and contextual blending process for blending of foodstuff.

BACKGROUND

Blenders and blending systems are often used to blend and processfoodstuffs. Frozen, frosty, or icy drinks have become increasinglypopular. Such drinks include the traditional shakes, and the morerecently popular smoothies. Shakes, or milk shakes, are typically formedof ice cream and/or milk, and flavored as desired, with or withoutadditives, such as candies, chocolates, peanut butter, fruits, etc.Milkshakes typically are available at most fast-food restaurants, suchas burger chains, and may be made by special machines, or hand-madeusing mixers.

Smoothies tend to be healthier, and may be formed of ice, frozen yogurt,and/or sorbet. Smoothies may include additives such as fruits, fruitjuice, vegetables, vitamins, supplements, etc. Smoothies typically areavailable from specialty chains or juice bars, and may be made withcommercial or restaurant-grade blender. Such drinks also may be made athome, using a personal blender.

One disadvantage with making any such drinks (examples of which areidentified above), or utilizing blenders, is the difficulty in blendingto a user's specific tastes or preferences due to the specificingredients required in some recipes. Another disadvantage with makingsuch drinks is the difficulty in measuring ingredients.

Users tend to add ingredients without measuring properly or byestimating amounts. Further, users may alter recipes to avoid or includecertain ingredients. These alterations may change a resultingconsistency or texture of a final blended drink. Users may not know howto change a blending process to meet their preferences. Further, usersmay not be able to determine dietary and fitness needs based on alteredrecipes.

Therefore, a need exists for improved systems and methods for blendingcontents in a blender. Further, there is a need for monitoringalterations in recipes and customizing blending systems for userpreferences.

SUMMARY

The following presents a summary of this disclosure to provide a basicunderstanding of some aspects. This summary is intended to neitheridentify key or critical elements nor define any limitations ofembodiments or claims. Furthermore, this summary may provide asimplified overview of some aspects that may be described in greaterdetail in other portions of this disclosure.

A blending system having various innovative features is provided herein.The blending system may include an ingredient component that mayidentify an ingredient and may determine a quantity of the ingredientthat is added to a blending container. The quantity of the ingredientmay be measured by a measuring system and communicated to the ingredientcomponent. A recipe component may generate suggestions for alteringcontents in a container to arrive at a user desired end state. Further,the recipe component may determine a blending process for blending thecontents in the container.

A method for contextual blending is also provided. The method mayprovide for blending foodstuff based on contents of the foodstuff. Themethod may include determining a type of ingredient and determining aquantity of the ingredient. The method may further include generating acontextual and/or intelligent blending process for blending theingredients according to a user's preference on the contents of thefoodstuff.

The following description and the drawings disclose various illustrativeaspects. Some improvements and novel aspects may be expresslyidentified, while others may be apparent from the description anddrawings.

DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate various systems, apparatuses,devices and methods, in which like reference characters refer to likeparts throughout, and in which:

FIG. 1 is a functional block diagram of a blending system in accordancewith various embodiments described here;

FIG. 2 is a functional block diagram of a blending system including adietary component in accordance with various embodiments described here;

FIG. 3 is a functional block diagram of a blending system including arating component in accordance with various embodiments described here;

FIG. 4 is an environmental view of a blending system in accordance withvarious embodiments described here;

FIG. 5 is a diagram of an exemplary interface of a blending system inaccordance with various embodiments described here;

FIG. 6 is a diagram of another exemplary interface of a blending systemin accordance with various embodiments described here;

FIG. 7 is a flow diagram of an exemplary method associated with ablender system in accordance with various embodiments described here;

FIG. 8 is a flow diagram of an exemplary method associated with ablender system that may generate a notification in accordance withvarious embodiments described here;

FIG. 9 is a flow diagram of an exemplary method associated with ablender system that may identify an ingredient and properties of theingredient in accordance with various embodiments described here;

FIG. 10 is a flow diagram of an exemplary method associated with ablender system that may generate a contextual blending process inaccordance with various embodiments described here;

FIG. 11 is an environmental diagram of an exemplary communication systemin accordance with various embodiments disclosed herein; and

FIG. 12 is a block diagram of a functional computer system in accordancewith various embodiments described here.

DETAILED DESCRIPTION

Reference will now be made to exemplary embodiments, examples of whichare illustrated in the accompanying drawings. It is to be understoodthat other embodiments may be utilized and structural and functionalchanges may be made. Moreover, features of the various embodiments maybe combined or altered. As such, the following description is presentedby way of illustration only and should not limit in any way the variousalternatives and modifications that may be made to the illustratedembodiments. In this disclosure, numerous specific details provide athorough understanding of the subject disclosure. It should beunderstood that aspects of this disclosure may be practiced with otherembodiments not necessarily including all aspects described herein, etc.

As used herein, the words “example” and “exemplary” mean an instance, orillustration. The words “example” or “exemplary” do not indicate a keyor preferred aspect or embodiment. The word “or” is intended to beinclusive rather an exclusive, unless context suggests otherwise. As anexample, the phrase “A employs B or C,” includes any inclusivepermutation (e.g., A employs B; A employs C; or A employs both B and C).As another matter, the articles “a” and “an” are generally intended tomean “one or more” unless context suggest otherwise.

Moreover, terms such as “access point,” “server,” and the likes, areutilized interchangeably, and refer to a network component or appliancethat serves and receives control data, voice, video, sound, or otherdata-stream or signaling-stream. Data and signaling streams may bepacketized or frame-based flows. Furthermore, the terms “user,”“customer,” “consumer,” and the like are employed interchangeablythroughout the subject specification, unless context suggests otherwiseor warrants a particular distinction among the terms. It is noted thatsuch terms may refer to human entities or automated components supportedthrough artificial intelligence (e.g., a capacity to make inference).Still further, “user,” “customer,” “consumer,” may include a commercialestablishment(s), such as a restaurant, restaurant chain, commercialkitchen, grocery store, convenience store, ice-cream shop, smoothierestaurant, or the likes.

“Logic” refers to any information and/or data that may be applied todirect the operation of a processor. Logic may be formed frominstruction signals stored in a memory (e.g., a non-transitory memory).Software is one example of logic. In another aspect, logic may includehardware, alone or in combination with software. For instance, logic mayinclude digital and/or analog hardware circuits, such as hardwarecircuits comprising logical gates (e.g., AND, OR, XOR, NAND, NOR, andother logical operations). Furthermore, logic may be programmed and/orinclude aspects of various devices and is not limited to a singledevice.

A network typically includes a plurality of elements that host logic. Inpacket-based wide-area networks (WAN), servers (e.g., devices comprisinglogic) may be placed at different points on the network. Servers maycommunicate with other devices and/or databases. In another aspect, aserver may provide access to a user account. The “user account” includesattributes for a particular user and commonly include a uniqueidentifier (ID) associated with the user. The ID may be associated witha particular mobile device and/or blender device owned by the user. Theuser account may also include information such as relationships withother users, application usage, location, personal settings, and otherinformation.

Embodiments may utilize substantially any wired or wireless network. Forinstance, embodiments may utilize various radio access network (RAN),e.g., Wi-Fi, global system for mobile communications, universal mobiletelecommunications systems, worldwide interoperability for microwaveaccess, enhanced general packet radio service, third generationpartnership project long term evolution (3G LTE), fourth generation longterm evolution (4G LTE), third generation partnership project 2,BLUETOOTH®, ultra mobile broadband, high speed packet access, x^(th)generation long term evolution, or another IEEE 802.XX technology.Furthermore, embodiments may utilize wired communications.

It is noted that, terms “user equipment,” “device,” “user equipmentdevice,” “client,” and the like are utilized interchangeably in thesubject application, unless context warrants particular distinction(s)among the terms. Such terms may refer to a network component(s) orappliance(s) that sends or receives data, voice, video, sound, orsubstantially any data-stream or signaling-stream to or from networkcomponents and/or other devices. By way of example, a user equipmentdevice may comprise an electronic device capable of wirelessly sendingand receiving data. A user equipment device may have a processor, amemory, a transceiver, an input, and an output. Examples of such devicesinclude cellular telephones (e.g., smart phones), personal digitalassistants (PDAs), portable computers, tablet computers (tablets), handheld gaming counsels, wearables (e.g., smart watches), desktopcomputers, etc.

It is noted that user equipment devices can communicate with each otherand with other elements via a network, for instance, a wireless network,or a wireline network. A “network” can include broadband wide-areanetworks such as cellular networks, local-area networks, wirelesslocal-area networks (e.g., Wi-Fi), and personal area networks, such asnear-field communication networks including BLUETOOTH®. Communicationacross a network may include packet-based communications, radio andfrequency/amplitude modulations networks, and the likes. Communicationmay be enabled by hardware elements called “transceivers.” Transceiversmay be configured for specific networks and a user equipment device mayhave any number of transceivers configured for various networks. Forinstance, a smart phone may include a cellular transceiver, a Wi-Fitransceiver, a BLUETOOTH® transceiver, or may be hardwired. In thoseembodiments in which it is hardwired, any appropriate kind or type ofnetworking cables may be utilized. For example, USB cables, dedicatedwires, coaxial cables, optical fiber cables, twisted pair cables,Ethernet, HDMI and the like.

It is noted that the various embodiments described herein may includeother components and/or functionality. It is further noted that whilevarious embodiments refer to a blender or a blender system, variousother systems may be utilized in view of embodiments described herein.For example, embodiments may be utilized in food processor systems,mixing systems, hand-held blending systems, various other foodpreparation systems, and the likes. As such, references to a blender,blender system, and the like, are understood to include food processorsystems, and other mixing systems. Such systems generally include ablender base that may include a motor, a controller, a display, a memoryand a processor. Further, such systems may include a blending containerand a blade assembly. The blade assembly, the blending container, andthe blender base may removably or irremovably attach. The blendingcontainer may be powered in any appropriate manner, such as disclosed inU.S. patent application Ser. No. 14/213,557, entitled Powered BlendingContainer, which is hereby incorporated by reference. Foodstuff may beadded to the blender container. Furthermore, while blending of“ingredients,” “contents” or “foodstuffs” is described by variousembodiments, it is noted that non-food stuff may be mixed or blended,such as paints, epoxies, construction material (e.g., mortar, cement,etc.), and the likes. Further, the blending systems may include anyhousehold blender and/or any type of commercial blending system,including those with covers that may encapsulate or partiallyencapsulate the blender. Further, commercial blending systems mayinclude an overall blending system, such as a modular blending systemthat may include the blender along with other components, such as acleaner, foodstuff storage device (including a refrigerator), an icemaker and/or dispenser, a foodstuff dispenser (a liquid or powderflavoring dispenser) or any other combination of such.

As used herein, the phrases “blending process,” “blending program,” andthe likes are used interchangeably unless context suggest otherwise orwarrants a particular distinction among such terms. A blending processmay comprise a series or sequence of blender settings and operations tobe carried out by the blending device. In an aspect, a blending processmay comprise at least one motor speed and at least one time interval forthe given motor speed. For example, a blending process may comprise aseries of blender motor speeds to operate the blender blade at the givenspeed, a series of time intervals corresponding to the given motorspeeds, and other blender parameters and timing settings. The blendingprocess may further include a ramp up speed that defines the amount oftime the motor takes to reach its predetermined motor speed. Theblending process may be stored on a memory and recalled by orcommunicated to the blending device.

Moreover, blending of foodstuff or ingredients may result in a blendedproduct. Such blended products may include drinks, frozen drinks,smoothies, shakes, soups, purees, sorbets, butter (nut), dips or thelikes. It is noted that various other blended products may result fromblending ingredients. Accordingly, terms such as “blended product” or“drink” may be used interchangeably unless context suggests otherwise orwarrants a particular distinction among such terms. Moreover, such termsare not intended to limit possible blended products and should be viewedas examples of possible blended products.

Aspects of systems, apparatuses or processes described herein generallyrelate to blending or mixing systems. In an embodiment, an ingredientcomponent may monitor ingredients added to a blending container. Theingredient component may determine a quantity (e.g., volume, weight,etc.) and state(s)/status(es) (e.g., cooked, raw, frozen, etc.) of eachingredient. A recipe component may access a stored recipe or dynamicallycreate a recipe based on a blending preference, such as a user'sblending preference or a predefined preference. The recipe component maycompare added ingredients, quantities, and states of ingredients with astored recipe or user preferences to determine a blending process (e.g.,blade speed, blending pattern, timing, etc.). Further, the recipecomponent may suggest adding certain ingredients, adding more orspecific types of liquids, more or specific types of solids, or otheritems (chia seeds) to achieve a predefined or preferred blending result.

Another aspect of the disclosed subject matter relates to determiningdietary or fitness goals based on a history of utilized recipes anddefined rules. A user may set preferences for fitness or dietary goals(e.g., calories, etc.). The fitness/dietary goals may include limits orthresholds on intake of certain substances or properties of substances(e.g., sugars, fats, calories, sodium, etc.). A recipe may be generatedand/or suggestions for altering a recipe may be generated based on thefitness/dietary goals. For instance, actual recipes that a user hasutilized may be stored and systems or methods described herein maysuggest substitution of ingredients based on the fitness/dietary goalsand/or other user preferences.

In an example, an intelligent blending system may include a blenderdevice, a user device, and a scale or measurement device that may beexternal to or part of the blender device or the user device. The usermay interact with the various devices by providing input. In an aspect,the user may enter blending preferences into a user device. Thepreference may include a level of thickness, texture, a desiredtemperature, calorie or other dietary levels, and the likes of the endproduct. As the user selects ingredients to add to the blending device,the user may input the type of ingredient and/or status of theingredient to the user device. Further, the scale may measure a quantityof each ingredient and communicate the quantity to the user device.Based on the input, the user device may determine a blending process toachieve a user's goal or preference. The user device may communicate theblending process to the blending device and the blending device mayimplement the blending process. It is noted that blending preferencesmay also be based on predefined preferences, dynamically determinedpreferences and/or preferences received from other users.

Referring now to FIG. 1, there depicted is a block diagram of afunctional blending system 100 that may generate and/or implementintelligent/contextual blending processes based on actual ingredients.Blending system 100 may primarily include contextual blending system106, memory 102, and processor 104. Memory 102 may be configured forstoring computer executable components such as an ingredient component110, a measuring component 120, account component 130, and a recipecomponent 140. Processor 104 may facilitate operation of the computerexecutable components. It is noted that system 100 may include one ormore devices, such as a user device, a blender device, and a scale. Itis further noted that one or more devices may comprise, at least inpart, the various components. For instance, a single component of system100 may be comprised by one or more devices. While shown as separate ordistinct components, the components of system 100 may be comprised byone or more components. Further, the system 100 may include a pluralityof blending devices that may be linked together through a network andtransrecievers. These blending devices may be operatively linked with aserver that may operate or otherwise update the plurality of blendingdevices.

Ingredient component 110 may monitor ingredients added to a blenderdevice (or a plurality of blending devices). In an aspect, ingredientcomponent 110 may receive input 114 comprising data that describes aningredient. The data may be user input in the form of text, voice input,selection of a prompt (e.g., user selecting a check box, etc.), or thelikes. For example, the user may type “spinach” via an input device(e.g., touch screen, keyboard, etc.) of system 100. In another example,the user may speak a term or phrase into a microphone and ingredientcomponent 110 may utilize a speech recognition process to determine theidentity or other aspects of the ingredient. Further examples allow auser to scroll through a list of ingredients and select a representationof spinach (e.g., textual, graphical, etc.).

In another aspect, ingredient component 110 may include or maycommunicate with other systems, such as cameras, optical scanningdevices, optical scanners, spectrometer, multi-wave length scanner,electronic noses, or the likes. Based on input from the other systems,ingredient component 110 may determine an identity of an ingredient.Ingredient component 110 may utilize image recognition techniques toidentify an image received as input 114. For example, a user may utilizeuser equipment devices, such as a smart phone or other camera device tocapture an image of one or more ingredients. Ingredient component 110may receive the image and identify one or more ingredients in the image.In another aspect, identified ingredients may be added to a list ofingredients for a current blending process. It is noted that ingredientcomponent 110 may utilize other methods or processes of identifying aningredient, such as scanning a barcode, label, radio frequencyidentification (RFID) tag, or other identifier on a product or productpackaging. This may be particularly useful in a commercial blendingsystem whereby prepackaged foodstuff ingredients are used and stored foruse with the blending system. The prepackaged foodstuff may include atleast one of the aforementioned devices to communicate with the system100 to identify the contents of the prepackaged foodstuff.

In another example, ingredient component 110 may include or communicatewith an electronic nose that may analyze a headspace (e.g., portion) ofa sample (e.g., airborne sample). For instance, an electronic nose maygather a sample and ingredient component 110 may detect a presence andan amount of a chemical in the headspace. In one implementation, theelectronic nose may include a sensory array or film. The sensory arraymay react to various chemicals within the headspace. The reaction maycause a change in physical or electrical properties of the sensoryarray. In one example, absorption of the chemicals in the headspacecauses physical alterations of the various sensors in the sensory array.Each sensor or a sensory array may react differently to the variouschemicals. Ingredient component 110 may transform the reactions of thesensory array into a digital signal. The digital signal may be computedbased on a statistical model. For example, an organic ultra-thintransistor chemical sensor having a channel that consists of one or moremonolayers may be employed. The organic ultra-thin film transistorchemical sensor may have monolayer thin film channels that act as highlysensitive detectors of trace levels of organic vapors and may performquantitative vapor analysis. The organic ultra-thin film may bepermeable to a chemical analyte of interest. Based on the analyzedheadspace ingredient component 110 may identify the type of ingredient.

In at least one embodiment, ingredient component 110 may determine aquantity associated with an ingredient. The quantity may be based onreceived input 114. Input 114 may be user input, such as a user enteredvolume, mass, or the likes. Further, the quantity may be derived from animage. For instance, ingredient component 110 may recognize a gradientmark on a measuring device (e.g., measuring cup, etc.) or a quantityindicator on a packaging.

In another aspect, the quantity may be based on a measurement of theingredient from measuring component 120. Measuring component 120 mayinclude a scale, sensor, or other device capable of determining aquantity. For instance, measuring component 120 may include a scale thatmeasures a weight or mass. It is noted that the scale may be comprisedwithin a blending device or may be a standalone device. In variousembodiments, measuring component 120 may determine a weight of aningredient and may communicate, via a wireless or wired connection, theweight to ingredient component 110. In some instances, measuringcomponent 120 may not have wireless or wired communication capabilities.In such instances, a user may read a measurement from a display ofmeasuring component 120 and may supply the measurement as input 114 tosystem 100, such as through an interface (e.g., touch screen, etc.). Inembodiments, a user may override measurement data from measuringcomponent 120. For instance, a user may review a weight of an ingredientand may alter the weight.

In embodiments, ingredient component 110 may receive or determine statusinformation associated with ingredients. Status information may includeinformation about a state of an ingredient. Such states may include, forexample, raw, cooked (e.g., level of doneness, type of cooking, etc.),frozen, thawed, temperature, fresh, canned, sliced, chopped, skinned,whole, and the likes. In some instances, certain ingredients may havespecialized or specific statuses not available to other ingredients. Forinstance, some fruits or vegetables may be associated with statuses suchas peeled, not-peeled, cored, not-cored, trimmed of leaves, etc. Otheringredients, such as liquid ingredients (e.g., water, milk, etc.) maynot be associated with such statuses.

It is noted that an ingredient may be associated with multiple statuses;however, some statuses may be exclusive of other statuses. That is, aningredient may not be both raw and cooked. As described above,ingredient component 110 may receive input 114 from a user, an imagecapturing device, an electronic nose, or another device. Ingredientcomponent 110 may utilize such input 114 to determine or drive a status.For example, ingredient component 110 may utilize image recognition todetermine that an ingredient is from a can, a frozen package, aprepackaged container, or the likes.

Furthermore, ingredient component 110 may utilize or comprise othersystems. For instance, ingredient component 110 may include orcommunicate with a thermometer. The thermometer may be comprised withina larger device, such as a scale, container, blender device, or astand-alone device. In some embodiments, the thermometer may becomprised within a user device (e.g., such as a laser thermometer,etc.). Further, in some embodiments, the thermometer may be included inthe container itself. Ingredient component 110 may utilize a temperatureto determine whether an ingredient is at least partially frozen, cooked,and/or thawed. The thermometer may be of any configuration, including aninfrared thermometer. Further, in an aspect hereof, the thermometer mayinclude a near field communication device that is able to read thetemperature of the contents within the container and communicate such toa processor (such as one in the blending device) or to a user device.

It is noted that system 100 may utilize various other statuses and mayinclude or communicated with various other devices or systems notdescribed for sake of brevity. Such statuses and systems are consideredvariations within the scope and spirit of the described subject matter.

Ingredient component 110 may also determine or monitor an order ofaddition of ingredients. In an aspect, ingredient component 110 mayrecord a time associated with an ingredient being added to a containerof a blender device. In some embodiments, the ingredient component 110may determine a time based on information received from measuringcomponent 120. For instance, a user may enter ingredients and associatedquantities into ingredient component 110. At a later time, the user maybegin to add the ingredients to a container. As the weight of thecontainer changes, ingredient component 110 may determine the order ofaddition of the ingredients based on data from measuring component 120.

In an aspect, ingredient component 110 may analyze what has been addedand identify for the user that the identified end product is notobtainable based upon the recipe being used. Further, the ingredientcomponent 110 may notify the user or may modify (or send notification toso modify) the recipe or processing parameters based upon theingredients added. For example, the ingredient component 110 may analyze(such as through any manner, including, without limitation thosedescribed above) the added ingredients. The ingredient component 110 mayidentify that too much of a particular ingredient was added that wouldresult in a consistency that is not preferred. The ingredient component110 may modify directly or send notification to modify the blending timeto account for the over-added or under-added ingredient.

Account component 130 may determine user preferences based on receiveddata, historical data, and the likes. In at least one embodiment, userpreferences may be stored in a user account (e.g., via memory 102). Auser may manually alter preferences and/or account component 130 mayautomatically alter preferences based on inferences. Furthermore,account component 130 may manage dietary and/or fitness goals orachievements as described in more detail herein.

In an aspect, a user may provide explicit information related to userpreferences. For instance, a user may provide, and account component 130may receive, information associated with preferences related to variousaspects of blending contents. For example, a user may provideinformation (e.g., input 114) related to preferred consistency ortexture (e.g., thickness, thinness, etc.), time of blending (e.g.,fastest blend, etc.), temperature, calories or other dietaryinformation, and the likes. The user may specify preferences forspecific types of blending recipes, such as for smoothies, frozendrinks, milk shakes, health smoothies, raw smoothies, etc. This mayfurther include a plurality of blending devices that may be networkedtogether. In these aspects, the input 114 may be a central userequipment device connected through a network and/or server that may beused to modify a recipe that is used by the plurality of blendingdevices to achieve a desired blend. The account components 130 of theblending devices may store the revised recipes in their memories. Thismay provide a mechanism by which a retail chain, restaurant (especiallya multi-location one), or the likes may modify a predefined recipe forthe entire plurality of blending devices that may be located atdifferent locations through a single input 114, which may be located ata central or regional office or location.

In another aspect, account component 130 may determine or monitor auser's preferences based on a history associated with the user. Thehistory may be related to a history of made items, rankings associatedwith items, or the likes—such as through a restaurant chain or thelikes. Such will be described in more detail herein. In embodiments,account component 130 may also include additional information such as adevice ID, user ID, devices associated with a user (e.g., blenderdevices, scales, fitness equipment, etc.), social connections (e.g.,friends, acquaintances, personal trainers, etc.), medical conditions(e.g., allergies, sensitives, etc.), and the likes.

Recipe component 140 may store (e.g., via memory 102) recipes forvarious drinks, smoothies, shakes, or the likes. Such recipes may bepredetermined (e.g., from database of recipes), received from adifferent device (e.g., shared by other user devices), or the likes. Insome embodiments, the recipe component 140 may receive or have inputteddirectly therein, a recipe that may be prescribed by a doctor, trainer,dietician, a regional or central office, or any other third party. Forexample, the doctor may prescribe the recipe or a plurality of recipesfor a particular patient and may send directly to the blending devicethrough the account component 130 for a user. This may be accomplishedthrough a software program or app that the doctor may access through hisor her user equipment device, through the patient's user equipmentdevice, or any other kind of electronic storage device that the user mayoperatively couple with his or her user equipment device. The recipe maybe fixed in that that patient cannot modify it or it may allow thepatient to modify based upon his or her preferences as describedherein—such as for preferred consistency, texture or the like.Similarly, a central office of a restaurant chain may modify a currentor a plurality of current recipes or add or delete a recipe or aplurality of recipes for use by the entire restaurant chain or a set ofpredefined locations. The modified, added or deleted recipes may be sentdirectly to each of the blending devices through the account component130, such as through a server and network. This may be accomplishedthrough a software program or app that the central office may accessthrough its user equipment device, through the particular restaurant'suser equipment device that may include transreceivers, or any other kindof electronic storage device that the may be operatively coupled withthe blending device, including without limitation through a memorydevice provided to each such location. The recipe may be fixed in thatthe particular restaurant location cannot modify it or it may allow therestaurant location to modify based upon regional preferences.

A recipe may include information associated with ingredients (identity,quantity, status, etc.) and a blending process (e.g., power settings,blade speed, blending pattern, etc.). In an aspect, recipe component 140may alter a recipe based on a user preference and/or device datareceived from account component 130. For example, a user may desire adetermined level of thickness and may be associated with a particularmake and model of a blender device. Based on the user's preference andthe blender device, recipe component 140 may customize the recipe forthe user (e.g., alter a quantity of an ingredient, add/remove aningredient, etc.). The customized recipe may be stored, for example, inmemory 102. For example, if a restaurant chain upgrades certain of itsblending devices, the recipes may need to be modified for thoselocations with the upgraded blending devices. The recipe components 140of the upgraded blending devices may alter the previous recipes toaccount for the functionality of the upgraded blending devices.

In various aspects, a user may not follow a predetermined or presetrecipe. Rather, the user may freelance with different ingredients,quantities, statuses of ingredients, and the likes. For instance, a usermay realize that they do not have a particular ingredient in theirrefrigerator or cupboard or that may otherwise be available in therestaurant. Thus, the user decides to add other ingredients that theyhave on hand. In this instance, recipe component 140 may generate acustom recipe based on the user's preferences and the added ingredients.For instance, ingredient component 110 may determine added ingredientsand recipe component 140 may determine a blend of such ingredients thatresults in an attribute to which the user has a low affinity, such as athick final product or drink or a drink (or other end product) thatwould not blend or otherwise would not result in the appropriate blend.Recipe component 140 may suggest addition of an ingredient and/oraltering amounts of an ingredient. Suggestions may also includesuggestions for altering preparation of ingredients, such as chop,slice, cook, thaw, and the likes. Further still, recipe component 140may allow a user to select or otherwise modify an existing recipe basedupon the ingredients the user may possess. The user may identify, suchas in any appropriate manner, the ingredients on hand. The recipecomponent 140 may, based upon these ingredients, provide arecommendation for a recipe or may alter another recipe based upon theingredients on hand. Further, the recipe component 140 may recommend toa user or may automatically alter a state of the resultant blend or maysuggest or instruct a user to add ingredients to solve a problem relatedto the blend, e.g., instruct the user to add chia seeds because theresultant blend contains too much foam. In such embodiments, the usermay communicate with the blending system 100 to identify the issues orproblems with the resultant blend in any appropriate manner, such asthrough a user equipment device, including a computer, laptop, tablet,smart phone, the blending system 100 directly or any other appropriatemanner.

Furthermore, recipe component 140 may generate a blending processcomprising instructions for a blender device (e.g., power settings,blade speeds, blending patterns, timing information, etc.). Inembodiments, recipe component 140 may determine the blending processbased on determining attributes associated with content to be blended(e.g., quantities, statuses, characteristics, ratios, etc.). Forinstance, recipe component 140 may include processes that determine ablending process based on determining a consistency/texture (e.g.,thickness, thinness, etc.) associated with ingredients, a ratio ofliquids to solids, a time associated with blending, a power usageassociated with blending, a temperature change associated with blending,and the likes. It is noted that recipe component 140 may be configuredto alter (e.g., optimize) attributes based on a users preference. Forexample, recipe component 140 may suggest additional ingredients toalter a consistency, texture or temperature associated with a blendedproduct or ingredients to be blended. In some embodiments, recipecomponent 140 may store and/or determine characteristics associated withingredients, such as consistency of a blend of the ingredient, a type ofthe ingredient (e.g., solid, liquid, thickening agent, etc.), and thelikes. Recipe component 140 may compare characteristics of various addedingredients to determine resulting characteristics of a blended productbased on a blending process.

Such processes may take the context of what is being blended in regardsto the ingredients, statuses of ingredients, quantities of ingredients,and personal preferences or recommended consistencies or eventtemperature and may create a custom blending process or program based onthose ingredients, statuses, quantities, order of addition ofingredients, and preferences to meet target thresholds. It is noted thatvarious aspects may alter attributes of a finished product. For example,use of a fresh (not caned or frozen) raw carrot may result in adifferent consistency than use of a fresh (not caned or frozen) cookedcarrot. Further, a raw and canned carrot may result in a differentconsistency than a raw and non-canned carrot. Moreover, a thawed (e.g.,previously frozen) carrot may result in a different consistency than araw (e.g., never frozen) carrot.

Moreover, recipe component 140 may generate or select one or more setsof instructions for a recipe, such as a blending process. One or moreblending processes may be stored, such as in memory 102. For example,memory 102 may store a set of preconfigured blending processes. Theblending processes may comprise a series or sequence of blender settingsand operations to be carried out by the blending device. For example, ablending process may comprise a series of blender motor speeds tooperate the blender blade at the given speed, a series time intervalscorresponding to the given motor speeds, and other blender parametersand timing settings. The blending processes may further include a rampor ramp up period that defines the amount of time it takes or the rateat which the motor gets up to the predetermined motor speed.

It is noted that a blending process may also comprise instructions thatgenerate notifications for actions to be executed by a user, such asaddition of an ingredient or increasing of a motor speed (e.g., if theblender device cannot increase the speed automatically). For example, ata given period or time, the blending process may instruct a blenderdevice to stop a motor. The blending process may then instruct aninterface (e.g., a screen of a smart phone or tablet) to display aprompt that requests a user to add an ingredient (e.g., “please add 1cup of ice”).

In at least one embodiment, recipe component 140 may select a presetblending processes and/or create a custom blending process based on thecontext of what is being blended in regards to the ingredients, statusesor ingredients, quantity of ingredients, personal preferences,recommended consistencies or recommended temperatures. For instance, apreset blending process may be selected to achieve a desired consistencyor texture based on the context of what is being blended. By way of anon-limiting example, if one is making a relatively course dip, the usermay want the consistency or texture of the end product to be thicker asopposed to thinner or potentially more or less coarse. This may beparticularly useful in producing a dip like salsa. In another instance,recipe component 140 may create a customized blending process foroptimal user satisfaction based on the context of what is being blended.

In another embodiment, recipe component 140 may generate a set ofblending processes to be selected by a user. For instance, recipecomponent 140 may generate blending processes that focus on speed,consistency, temperature, a balance of one or more other aspects, or thelikes. The user may select a desired blending process based on theuser's preferences.

In an aspect, recipe component 140 may produce an end product that it isat or otherwise between a preferred temperature range. For example, auser may wish to produce a soup at a predetermined temperature, e.g.,150 degrees Fahrenheit. The recipe component 140 may control theblending process so that the resultant end product is at a predeterminedtemperature or between a range of temperatures, e.g., 110 degreesFahrenheit to 190 degrees Fahrenheit. In some embodiments, a thermometermay be operatively coupled with the recipe component 140 or otherwise tothe blending device such that once a preferred or predefined temperatureis reached, the recipe component 140 will instruct completion of theblending process. In an aspect, the container may include a thermometerformed therein. The thermometer may include an NFC chip—e.g., one thatuses electromagnetic induction between two loop antennas located withinthe container's near field and the recipe component 140 (or morespecifically the blending device), effectively forming an air-coretransformer. The NFC chip may identify the temperature of the contentswithin the container and may provide enough power to operate thethermometer therein. Once the contents reach a predeterminedtemperature, the recipe component 140 (or other applicable component asdescribed herein) may instruct the blending device to cease blending—seebelow for more details. This will generally prevent over-blending of thecontents and provides a mechanism to automatically finish the blendingprocess based upon the predetermined temperature. Further, the NFC chipmay power the container. This may permit a digital read out or othertype of display to be included on the container to indicate thetemperature of the contents of the container. While a thermometer ismentioned above, the present teachings are not limited to such. Thecontainer may include any kind of sensor that may sense or detect anyaspect of the ingredients add, the blending process (including time,speed of material within the container, viscosity of the material,opaqueness of the material, etc.). The sensor may be integrated into orotherwise attached with the container.

In an aspect, recipe component 140 may generate output 112 asinstructions to implement the recipe or blending process. A blendingdevice may be capable of receiving and executing instructions of theblending process. It is noted that communicating the recipe, or otherinformation, may comprise any wired or wireless connection, including,without limitation, Wi-Fi communication, cellular communication, wiredcommunications, or the likes. For instance, system 100 may utilize nearfield communication. In near field communication data may be exchanged(e.g., recipes) between devices when they are brought into a predefinedclose proximity of each other, including, without limitation thecontainer and the process of the blending device.

In an aspect, recipe component 140 (as well as other components ofsystem 100) may utilize artificial intelligence, statistical models, orother processes and/or algorithms. In embodiments, recipe component 140may utilize classifiers that map an attribute vector to a confidencethat the attribute belongs to a class. For instance, recipe component140 may input attribute vector, x=(x1, x2, x3, x4, xn) mapped tof(x)=confidence(class). Such classification can employ a probabilisticand/or statistical based analysis (e.g., factoring into the analysisaffinities and ingredient attributes) to infer an action that a userdesires to be automatically performed. In various embodiments, recipecomponent 140 may utilize other directed and undirected modelclassification approaches include, e.g., naïve Bayes, Bayesian networks,decision trees, neural networks, fuzzy logic models, and probabilisticclassification models providing different patterns of independence.Classification may also include statistical regression that is utilizedto develop models of priority.

In an aspect, account component 130 may utilize artificial intelligence,statistical models, or other processes and/or algorithms. Inembodiments, account component 130 may utilize classifiers that map anattribute vector to a confidence that the attribute belongs to a class.For instance, account component 130 may input attribute vector, x=(x1,x2, x3, x4, xn) mapped to f(x)=confidence(class). Such classificationcan employ a probabilistic and/or statistical based analysis (e.g.,factoring into the analysis affinities and ingredient attributes) toinfer an action that a user desires to be automatically performed. Invarious embodiments, account component 130 may utilize other directedand undirected model classification approaches include, e.g., naïveBayes, Bayesian networks, decision trees, neural networks, fuzzy logicmodels, and probabilistic classification models providing differentpatterns of independence. Classification may also include statisticalregression that is utilized to develop models of priority. Furtherstill, classification may also include data derived from another system,such as cameras, optical scanning devices, optical scanners,spectrometer, multi-wave length scanner, electronic noses, or the likes.

In accordance with various aspects of the subject specification, anexample embodiment may employ classifiers that are explicitly trained(e.g., via a generic training data) as well as implicitly trained (e.g.,via observing user behavior, blending information, user preferences,historical information, receiving extrinsic information). For example,support vector machines may be configured via learning or training phasewithin a classifier constructor and feature selection module. Thus, theclassifier(s) may be used to automatically learn and perform a number offunctions, including but not limited to determining, according to addedingredients (e.g., states and/or quantities), additional ingredients toadd to meet user preferences, blending processes associated withfunctions of a blender motor, suggested recipes, target goals fordietary or fitness needs, and the likes. This learning may be on anindividual basis, i.e., based solely on a single user, or may applyacross a set of or the entirety of the user base. Information from theusers may be aggregated and the classifier(s) may be used toautomatically learn and perform a number of functions based on thisaggregated information. The information may be dynamically distributed,such as through an automatic update, a notification, or any other methodor means, to the entire user base, a subset thereof or to an individualuser.

In an aspect, contextual blending system 106 may generate output 112 inthe form of data. The data may include instructions that control ablender device, information associated with user preferences, requestsfor information, and the likes. Furthermore, output 112 may compriseinstructions to control a user interface, such as a touch screen of amobile device. The output 112 may control other user interfaces such asaudible devices (e.g., speakers, microphones, etc.), visual devices(e.g., light emitting diodes (LED), etc.), or other user interfaces.This data may be aggregated to create and provide to users popularrecipes. The data may be based upon how users modify existing recipes.By way of a non-limiting example, if a majority of users modify aparticular recipe the data related to such modification may beaggregated such that the recipe may be automatically modified and themodified recipe provided to other users whether dynamically orotherwise. By way of a further non-limiting example, if a plurality ofrestaurants in a chain modifies a particular recipe in a majority ofblends, the modified blend may be aggregated and the recipeautomatically updated for the entire chain or a preset number of suchrestaurants of the chain.

While various embodiments or examples may refer to a home or personalblender device, it is noted that commercial blender devices may beutilized. Furthermore, embodiments described herein may be utilized onretail settings. For instance, users may order a blended product at arestaurant or retail store. The user may provide identification, such asvia a user device. System 100 may enable customizing of the users drinkbased on stored preferences associated with the user ID.

Turning now to FIG. 2, there depicted is a block diagram of a functionalblending system 200 that may generate intelligent blending processesbased on actual ingredients and coordinate dietary goals. Blendingsystem 200 may primarily include contextual blending system 206, memory202, and processor 204. Memory 202 may be configured for storingcomputer executable components such as an ingredient component 210, ameasuring component 220, account component 230, a recipe component 240,and a dietary component 250. Processor 204 may facilitate operation ofthe computer executable components.

As noted, system 200 may include one or more devices, such as a userdevice, a blender device, and a scale. It is further noted that likenamed components of various systems described herein may comprisesimilar or identical aspects and/or functionality unless contextsuggests otherwise or warrants a particular distinction among suchcomponents. For instance, ingredient component 110 and ingredientcomponent 210 may comprise substantially similar aspects orfunctionality. Moreover, system 200 may include a different number ofcomponents and may be combined with any other features described herein.

Dietary component 250 may monitor blending activity, dietaryinformation, and final blended products associated with a user. Forinstance, dietary component 250 may monitor a history of whatingredients were blended, how often a user blends contents, and thelikes. Furthermore, dietary component 250 may include and/or communicatewith other fitness or dietary systems. In an example, dietary component250 may be comprised within a wearable device, such as a smart watch. Insuch an instance, dietary component 250 may monitor a user's activity(e.g., walking, running, exercise, etc.).

In another aspect, dietary component 250 may receive input (e.g., input214) from dietary or fitness systems. The input may include exerciseinformation, information about other consumed food or drinks, and thelikes. The dietary or fitness systems may be any dietary or fitnesssystem capable of communicating via a wireless or wired connection. Suchdietary or fitness system may include a wearable device, a Wi-Ficonnected fitness device (e.g., treadmill, gaming counsel, etc.), agaming device (e.g., a fitness game or program on a gaming device), acomputer, laptop, smartphone, tablet or the likes.

In embodiments, dietary component 250 may receive input 214 as userprovided input. User input may include information manually entered by auser, such as a user's exercise history, meals, calorie intake, and thelikes. Furthermore, user input may include user defined goals or dietarythresholds. Goals may include dietary thresholds or fitness goals.Fitness goals may include, for example, altering weight (e.g., weightloss or weight gain), increasing muscle mass, caloric intakes, and thelikes. Dietary thresholds may include levels associated with intake ofcalories, sodium, fat, vitamins, or the likes.

In another aspect, dietary component 250 may utilize information fromvarious other components to generate suggestions associated with goalsand/or thresholds. For instance, dietary component 250 may suggestalterations (e.g., substituting ingredients) in recipes to reducecaloric intake. In another example, dietary component 250 may generateinformation associated with an amount of exercise needed to burn off orutilize calories from blended ingredients. The suggestion may include atype(s) of exercise based on user's preferences or history. For example,if a user has an affinity to jogging, then dietary component 250 maygenerate a suggestion indicating an amount of jogging (e.g., distanceand/or time) needed to burn off the calories in the blended ingredients.

Moreover, dietary component 250 may communicate with other systems ordevices. For instance, dietary component 250 may transmit output 212 toan external system. The output 212 may include dietary goals, blendinghistory, and the likes. In another aspect, output 212 may include datainstructing a display device to output a graphical user interface asdescribed in more detail herein.

In at least one embodiment, dietary component 250 and other componentsof system 200 may receive data as input 214 from one or more serverdevices or other network devices. The data may comprise update data forupdating software, updating dietary information associated withingredients or potential ingredients, data generated by other users(e.g., user created recipes, friend requests, etc.), or the likes. Insome embodiments, dietary component 250 may store, via memory 202,dietary information associated with ingredients or may receive thedietary information from a remote storage device (e.g., a database).

In an aspect, receiving update data may comprise downloading and runninga software application. The software application may be capable ofconnecting to a network, such as the Internet. The software applicationmay be capable of accessing step-by-step recipes or blending programsfrom a remote database or website, such as www.vitamix.com, anddownloading the recipes or programs to the wireless device.

Turning now to FIG. 3, there depicted is a block diagram of a functionalblending system 300 that may generate intelligent blending processesbased on actual ingredients and user input. Blending system 300 mayprimarily include contextual blending system 306, memory 302, andprocessor 304. Memory 302 may store computer executable components suchas an ingredient component 310, a measuring component 320, accountcomponent 330, a recipe component 340, and a rating component 350.Processor 204 may facilitate operation of the computer executablecomponents.

As above, system 300 may include one or more devices, such as a userdevice, a blender device(s), and a scale. It is further noted that likenamed components of various systems described herein may comprisesimilar or identical aspects and/or functionality unless contextsuggests otherwise or warrants a particular distinction among suchcomponents. For instance, recipe component 140 and recipe component 340may comprise substantially similar aspects or functionality. Moreover,system 300 may include a different number of components and may includeany of the components described herein.

Rating component 350 may receive input 314 as user input regarding auser's affinity to a particular item, such as a recipe, a blendedproduct, an ingredient, or the likes. For example, a user may create ablended drink. After creating the drink, the user may decide whether ornot the user likes the drink or the blending process. The user may ratethe drink via an interface, such as via a user device, smart phone, ablender, or the likes. The rating may include, for example, an overallrating, a consistency rating, a flavor rating, a preparation timerating, and the likes. Instead of the user, a consumer of the drink mayrate the drink via any interface, such as a dedicated computer, tabletor the like in the location or through the consumer's user device, e.g.,computer, tablet, smart phone or the like.

In an aspect, the rating may include a number of tokens out of a numberof possible tokens (e.g., 3 out of 5 stars, etc.), a thumbs up or thumbsdown, a numerical score, or the likes. It is noted that various otherrating or ranking systems may be utilized. Such systems may includedifferent nomenclatures, subcategories, or the likes. For instance, aconsistency rating may comprise an overall consistency rating and auser's subjective opinion for improvement, such as “too thin,” “toothick,” “just right,” etc. The consistency rating may also indicate thetexture of the end product. Alternatively, a separate texture rating maybe used. In those embodiments, the texture rating may indicate whetherthe end product is “too coarse”, “not coarse enough,” “just right,” orany variation thereof.

In an embodiment, rating component 350 may determine what the user doesand does not like based on intrinsic or extrinsic data. As such, ratingcomponent 350 may utilize user input, statistical data representing ablending history or history of use. For example, as a user rates blendeddrinks, a pattern may develop. The pattern may indicate a user'spreference for or against at least one of ingredient, a combination ofingredients, a blending process, a time associated with blending,temperature, or the likes. For instance, a user may prefer a combinationof grapes and apples in a smoothie, but may otherwise be unaware of thepreference. Rating component 350 may infer or determine the user'spreference for the combination and may notify the user (e.g., such asvia a “favorites” or “suggestions” folder) or may automatically suggestusing the combination in recipes.

Rating component 350 may publish or share ratings and/or recipes withother users or systems. Publishing may comprise uploading the recipe toa server or other network device that is accessible by other devicesconnected to the server and/or with appropriate authority. For instance,a user may rate a blended drink and share their rating and their recipewith other users to which the user has a social connection (e.g.,friend, acquaintance, etc.). It is noted that sharing of information maybe disabled or may be prevent unless a user provides authorization forsuch sharing. Likewise, personal information may be retained private.This sharing may also include ratings from consumers of the drinks. Theconsumer ratings may be shared with the restaurant chain and therestaurant chain may modify a recipe based upon this consumer feedback.

FIG. 4 depicts a non-limiting plan diagram of a functional blendingsystem 400 that may provide intelligent blending. As depicted, system400 may comprise a user device 410, a blender device 420, and a scaledevice 430. It is noted that some or all devices depicted in FIG. 4 maynot be included in various embodiments. As such, FIG. 4 depicts at leastone or many envisioned embodiments.

It is noted that system 400 may comprise one or more of various othersystems described herein, such as systems 100, 200, and 300. Any one ofuser device 410, blender device 420 or scale device 430 may comprise allor some of the various components of such systems. By way ofillustration, user device 410 may comprise all or part of system 100.Furthermore, user device 410 may comprise all or part of ingredientcomponent 110, account component 130, and recipe component 140, whilescale device 430 and/or blender device 420 comprises all or part ofmeasuring component 120. In another example, blender device 420 maycomprise all or part of ingredient component 110, measuring component120, account component 130, and recipe component 140. As such,embodiments described herein are not limited to a certain device(s) orconfiguration among devices. Further, embodiments describing user device410, blender device 420 or scale device 430 performing particular actsare understood to be examples.

While shown as distinct devices, the various devices of system 400 maybe comprised by one or more device. In an example, blender device 420may comprise scale device 430. In another example, user device 410 maycomprise scale device 430. It is further noted that system 400 maycomprise other devices (e.g., access points, server devices, etc.) notshown for sake of brevity.

While user device 410 is depicted as a smart phone, it is noted thatuser device 410 may comprise one or more other devices. Such devices mayinclude wearable electronics (e.g., smart watches, etc.), laptopcomputers, desktop computers, tablet computers, gaming devices (e.g.,handheld gaming devices, set top boxes, etc.), and the likes. Likewise,blending device 420 and scale device 430 may comprise otherconfigurations or designs. Such devices are not limited to a particularmake or model. In an example, blending device 420 and scale device 430may comprise built in wireless capabilities or may be configured toreceive a wireless adapter that enables wireless communication.

User device 410, blender device 420 and scale device 430 may communicatewith each other and with other devices (not shown). In an aspect, thedevices may communicate via wireless or wired communications. Asdescribed above, such devices may utilize near field communicationtechniques to communicate when the devices are within a determineddistance of each other. It is noted that the exact method ofcommunication may vary depending on a desired configuration.

In an example, a user may follow a recipe that may be displayed by userdevice 410 or the user may create a recipe. When the user desires to addan ingredient, the user may enter a name of the ingredient via aninterface of user device 410. In another aspect, the user may use acamera of user device 410 to scan a label or take a picture of theingredient. User device 410 may identify (e.g., via ingredient component110, 210, 310, etc.) the ingredient via image recognition or the likes.

The user may also enter status information associated with theingredient. For instance, the user may provide input to user device 410about the state of the ingredient, such as cooked, raw, frozen, chopped,etc. The user may also place the ingredient on scale device 430. Scaledevice 430 may measure a quantity (e.g., weight) of the ingredient. Invarious embodiments, scale device 430 may communicate a measurement touser device 410. In other embodiments, a user may read an output fromscale device 430 and user device 410 may receive user input regardingthe output that is read by the user.

In some embodiments, the user may add the ingredient to a blender device420. In other embodiments, the user may wait to add the ingredient at alater time. It is noted that user device 410 may provide instructions onwhen to add an ingredient and the user may follow the instructions.

When a user is ready to begin blending (e.g., all initial ingredientsare added to the blender), the user may initiate the blending processvia an interface. The interface may be comprised by the user device 410,the blending device 420 or the scale device 430. In an aspect, theblending device 420 may follow a customized blending process that isbased at least in part on components of a recipe (e.g., ingredients,status of ingredients, quantity, etc.) and user preferences.

It is noted that user device 410 may provide output or notifications toa user during the blending, preparation, and/or creation of a recipe.For instance, user device 410 may provide a notification as an audibleand/or visual queue to a user. The notification may include a popup orfly out that provides a suggestion for altering a recipe to achieve auser's preference (e.g., “To reach your preferred consistency ortexture, try adding 1 more cup of liquid ingredients,” etc.). In atleast one embodiment, the notification may include computer generatedvoice output, audio-video output, or the likes.

In another aspect, user device 410 may provide notifications regardingerror or fault checking. The error checking may be related tomalfunctions or misconfigurations of devices (e.g., lid not engaged onblender device 420), forgotten ingredients, wrong ingredients, potentialallergens, or the likes.

FIGS. 5 and 6 are non-limiting user interfaces 500 and 600,respectively. While depicted as interfaces of user device 410, it isnoted that interfaces 500 and 600 may be interfaces of other devices(e.g., blender device 420, etc.). In embodiments, interface 500 and 600may be rendered by a user interface device, such as a monitor of touchscreen. In an aspect, a blending system (e.g., system 100, 200, etc.)may instruct the user interface device to render the interfaces 500 or600. It is noted that actions described with reference to interfaces 500or 600 may be accomplished via one or more other systems describedherein (e.g., system 100, 200, etc.). Furthermore, while embodiments mayreference user actions, it is noted that users (e.g., humans, etc.) maynot be required to perform such actions.

Different interfaces may be utilized to enable additional or differentfunctionality. Such interfaces may also provide different means oforganizing navigation, selection and the likes. The interfaces presentedherein are intended to provide examples of possible interfaces and arenot intended to limit the scope of various other embodiments.

Referring first to FIG. 5, interface 500 depicts a rendering of outputassociated with a blending system. The rendering may comprise one ormore buttons or selection tokens. Selection of such tokens may initiateother actions, such as rendering different screens and the likes. Asdepicted, interface 500 may include an add recipe token 520, a selectrecipe token 522, a see goals token 524, and a user account token 526.Each token may be selectable and may represent a certain set of actionsor responses to selection.

Add recipe token 520 may be associated with actions related to adding anew recipe, such as a user created recipe or a downloadable recipe. Inan aspect, in response to receiving user input regarding selection ofthe recipe, user device 410 may receive (e.g., via ingredient component310, etc.) additional input regarding ingredients. Further, selectrecipe token 522 may be associated with actions related to selecting anexisting recipe, such as a recipe stored in a memory (e.g., memory 302,etc.).

See goals token 524 may be associated with fitness or dietary goals. Inresponse to receiving input that represents a selection of see goalstoken 524, user device 410 may generate a rendering of user goals. Usergoals may be generated (e.g., via dietary component 250) over a periodof time and/or with respect to future needs. Moreover, usage reports anddietary/fitness progress may be presented to a user via a graph, chart,or the likes.

User account token 526 may be associated with maintaining, adding, orediting information associated with one or more users. For instance, auser may select the user account token 526 to add a new blending device,link devices, set preferences, and the likes. In an aspect,functionality associated with user account token 526 may be enabled viaaccount component 230.

Interface 600 is an example rendering for creating a custom recipe andblending process. For instance, a user may add various ingredients andstatuses of ingredients via interface 600 (e.g., through an ingredientcomponent 110, 210, etc.). Likewise, a user may initiate generation of ablending process and/or execution of a blending process (e.g., byblending device 420).

In an embodiment, interface 600 may include a set of control tokenscomprising add ingredients token 612, cancel token 614 (which may cancela current project), a blend token 616, and a consistency control token618. Furthermore, interface 600 may include an added ingredients area orelement 620. Added ingredients area 620 may comprise (if available) animage 622 or identity of an added ingredient and status tokens 628.

As an example, a user may interact with interface 600 to create a customrecipe. The user may select add ingredients token 612 to add aningredient to a list of added ingredients. The list off addedingredients may be presented in an added ingredients area 620. Asdescribed herein, the user may provide input such as text or voice inputregarding an identity of an ingredient. In another example, the user mayutilize user device 410 to capture an image of an ingredient, scan alabel or identifier associated with an ingredient, collect a headspacesample of an ingredient, or the likes. User device 410 may identify(e.g., via ingredient component 110, 210, 310, etc.) an ingredient basedon received information.

Once an ingredient is added, the user may be prompted to configure astatus of the ingredient via status tokens 628. The prompt may be anaudible, visual, or tactile (e.g., vibration, etc.) prompt. As describedin more detail above, the status of the ingredient may include variousfields and may be dependent on the identity of the ingredient. In thisexample, the ingredient is “sliced cucumber” and the user has select“raw” with a quantity of four. It is noted that the quantity may be anumber of items (e.g., four cucumbers), a weight (e.g., 4 ounces) asprovided via a measurement component (e.g., measurement component 120,220, 320, etc.), a volume (e.g., 4 cups), generalized measurements(e.g., one pinch, a handful, etc.), or the likes.

In embodiments, the user may also select a desired consistency viaconsistency token 618. While depicted as a slideable scale, consistencytoken 618 may take various forms, such as a numerical value, a ratingscale, or the likes. It is noted that consistency token 618 may bepreset based on user preferences. It is further noted that consistencytoken 618 may be constrained (e.g., via upper or lower bounds) based onadded ingredients and a make and model of a blending device. Forinstance, a recipe component (e.g., recipe component 140, 240, 340,etc.) may determine a maximum or minimum level of thickness given theadded ingredients and/or the blending devices being utilized. Suchmaximum and minimum levels may constrain selectable regions ofconsistency token 618. In some embodiments, the consistency token 618may account for the texture of the end product. In other embodiments, aseparate texture token (not shown) may be utilized. The texture tokenmay take various forms, such as a numerical value, a rating scale, orthe likes. It is noted that texture token may be preset based on userpreferences. It is further noted that texture 618 may be constrained(e.g., via upper or lower bounds) based on added ingredients and a makeand model of a blending device.

As a user adds ingredient, device 410 may render prompts to provideinformation or notify the user. The prompts may be popups, fly outs, orother visual prompts. For example, given a user's preference for aparticular consistency or texture, device 410 may determine (e.g., via arecipe component 140, 240, 340, etc.) what ingredients may be altered(e.g., added, removed, etc.) to achieve the user's desired consistencyor texture. Device 410 may generate a popup window to list thesuggestions. The popup window may be displayed in response to a useraction (e.g., selection of a “suggestions token”—not shown). In anotherexample, a prompt may indicate a dietary/fitness attribute such asexceeding a target calorie count for a recipe, an amount of exerciserequired to burn off the calories, or the likes.

In response to selection of blend token 616 (or another triggeringevent), device 410 may initiate generation (e.g., via a recipe component140, 240, 340, etc.) of a custom blending process based at least in parton contextual information associated with ingredients or a blendingdevice. Once a customized (e.g., optimized based on user preferences)blending process is created, a user may review the blending process andaspects associated with the blending process, such as estimate time,estimated temperature, and the likes.

In view of the subject matter described herein, methods that may berelated to various embodiments may be better appreciated with referenceto the flowcharts of FIGS. 7-10. While the methods are shown anddescribed as a series of blocks, it is noted that associated methods orprocesses are not limited by the order of the blocks. It is furthernoted that some blocks and corresponding actions may occur in differentorders or concurrently with other blocks. Moreover, different blocks oractions may be utilized to implement the methods described hereinafter.Various actions may be completed by one or more of users, mechanicalmachines, automated assembly machines (e.g., including one or moreprocessors or computing devices), or the like.

FIG. 7 depicts an exemplary flowchart of non-limiting method 700associated with a blending systems, according to various aspects of thesubject disclosure. As an example, method 700 may determine a blendingprocess based on contents of a blending device and blending preferences.Further, method 700 may facilitate implementation of the blendingprocess by the blending device.

At 702, identifying, by a system (e.g., via ingredient component 110),an ingredient associated with a set of ingredients to be blended.Identifying the ingredient may include determining an identity of theingredient based on received data. Received data may include user input(e.g., text input, voice input, selections from drop down menus, etc.),image input, scent input, or the likes. It is noted that a list oflibrary of identities may be stored in a database or memory store, whichmay be remote or local storage. Stored identities or related informationmay be matched in received data to determine the identity of theingredient.

At 704, analyzing, by the system (e.g., via recipe component 140), theset of ingredients and data describing a blending preference. Theblending preferences may include user defined preferences (e.g.,consistency preferences, blend time preferences, power consumptionpreferences, etc.), predetermined preferences, or the likes as describedherein. In an aspect, analyzing the set of ingredients and datadescribing the blending preference may include utilizing a program orprocess that balances (e.g., optimizes) one or more aspects of ablending process. In some embodiments, analyzing may include determiningratios (e.g., liquid to solid ratios, ratios associated withtemperature, or the likes).

At 706, determining, by the system (e.g., via recipe component 140),parameters for operation of a blending device based at least in part onthe analysis of the set of ingredients and the data. It is noted that asystem may utilize various algorithms or processes to determine theoperating parameters, as described herein. For instance, determining theparameters may comprise a statistical model that achieves a desiredresult based on selecting various parameters in light of ingredients andblending preferences. Further still, the system may determine or modifyparameters of the blending device based on input from the system, e.g.,input from a user device, the blending device, a container, a separateinput device, a sensor or the like. As described herein, the parametersmay include timing information, motor power parameters, motor speeds,ramp up periods, or the like.

At 708, transmitting, by the system (e.g., via recipe component 140),data instructing the blending device to blend the set of ingredientsbased at least in part on the parameters. For example, a user device maysend a generated blending process (e.g., operating parameters) to ablender device. In another example, a processor of a blending device maytransmit instructions to various controllers or components (e.g., motor,ASICs, etc.).

FIG. 8 depicts an exemplary flowchart of non-limiting method 800associated with a blending systems that may generate a notification,according to various aspects of the subject disclosure. As an example,method 800 may generated a suggestion for altering contents of a blenderand generate a notification of the suggestion.

At 802, monitoring, by a system (e.g., via recipe component 240 and/ordietary component 250), a history associated with a user entity, whereinthe history comprises data describe at least one of a blending history,a dietary history, or a fitness activity history. In embodiments, thehistory may comprise data describing previous blending instancesassociated with the user entity, exercising activity associated with theuser entity and the likes.

At 804, determining, by the system (e.g., via recipe component 240),data describing a user preference. Determining the data describing theuser preferences may include retrieving the data from a memory store,determining the data based on an analysis of historical data, or thelikes. As such, the user preferences may be explicitly defined by theuser entity or may be derived based on a history associated with theuser entity.

At 806, generating, by the system (e.g., via recipe component 240), asuggestion for altering a set of ingredients based on at least one ofthe data describing a blending preference, the history, or a userdefined threshold associated with a blended product. The suggestion mayinclude adding an ingredient, altering a state/status of an ingredient,altering rations, or the likes. For instance, the system may determinethe ratio of solids to liquids that may result in a blended product thathas a consistency that the user may not prefer, such as the blendedproduct will result in a thin drink. In such an instance, the system maygenerate a suggestion that the user add a thickening agent or more solidingredients to a blender device. Still further, the system may instructthe blending device to blend at a lower speed or for a shorter durationthan otherwise.

At 808, generating, by the system (e.g., via recipe component 240), anotification that conveys the suggestion. The notification may be apopup, fly out, banner message, or the likes. For instance, an interfaceof a smart phone or blending device may display the notification. Inanother example, the notification may be an audio or audio-visual queue.

FIG. 9 depicts an exemplary flowchart of non-limiting method 900associated with a blending systems that may identify an ingredient andproperties of the ingredient, according to various aspects of thesubject disclosure. As an example, method 900 may determine identitiesof ingredients or foodstuffs, quantities, and other aspects related to astate or status of the foodstuffs.

At 902, receiving, by a system (e.g., via identity component 310), dataassociated with foodstuff. The data associated with the foodstuff mayinclude image data (including, without limitation, visual light rangeand non-visual light range), user input (e.g., text data, selectiondata, voice input, etc.), scent data (e.g., chemical input), or thelikes. In an example, the user may utilize a camera to capture an imageof an ingredient.

At 904, identifying, by the system (e.g., via identity component 310),an identity of the foodstuff based on the data associated with thefoodstuff. The identity may include a name or unique identifierassociated. For instance, the user may capture an image of theingredient and the system may perform image recognition techniques todetermine an identity of the ingredient.

At 906, determining, by the system (e.g., via identity component 310), aquantity of the foodstuff based on an output of a measurement system(e.g., measurement component 320). The measurement system may include ascale or other device. In various embodiments, the output may becommunicated via a communication framework, via user input, or thelikes.

At 908, receiving, by the system (e.g., via ingredient component 310),at least one of a cooked status, a temperature state, or a preparationstatus of the foodstuff. A cooked status may include information thatdescribes whether foodstuff is cooked, raw, or the likes. In anotheraspect, a cooked status may include a degree or type of cooking (e.g.,seared, boiled, fried, etc.). A temperature state may include parameterssuch as frozen, thawed-previously frozen, cooked-warm, cooked-cooled, orthe likes. Further, a temperature state may include a measured orotherwise determine temperature such as through the container asdescribed above. Such temperatures may be a scaled or estimate oftemperature (e.g., hot, warm, cold, etc.), actual temperature (e.g.,Fahrenheit, Celsius, etc.), or other measurement techniques. Preparationstatus of the foodstuff may refer to whether the foodstuff is peeled,cut, chopped, sliced, etc. In an aspect, preparation statuses may beunique to one or more types of ingredients. For example, ice may have acrushed preparation status but may not have a peeled status.

FIG. 10 depicts an exemplary flowchart of non-limiting method 1000associated with a blending systems that may generate a contextualblending process, according to various aspects of the subjectdisclosure. As an example, method 1000 may monitor ingredients added toa blending device and may create customized blending processes based onthe monitoring.

At 1002, monitor, by a system (e.g., via user device 410), ingredientsadded to a blending device. Monitoring the ingredients may includemonitoring the ingredients as they are added to a blending device,monitoring ingredients as a user provides input regarding the additionof the ingredients, or the likes. In an aspect, monitoring may includestoring data (e.g. in a memory device).

At 1004, monitor, by the system (e.g., via user device 410), an order ofaddition of the ingredients. In embodiments, the system may monitoringredients as they are added and/or as a user indicates that theingredients are added. In some embodiments, a user may alter or overrideorders of addition. For example, a user may measure ingredients andprovide user input regarding the ingredients. The user may putingredients aside while measuring other ingredients. The user may, at alater time, add the ingredients to a blending device. In this example,the user may realize that they added ingredients in a different order.As such, the user may alter the order in a blending system (e.g.,blending system 100, etc.).

At 1006, determine, by the system (e.g., via user device 410), dietarygoals. Dietary goals may be determined based on stored information, userinput, information received from other systems (e.g., an application ona smart phone or other smart device) or the likes.

At 1008, generate, by the system (e.g., via user device 410),intelligent blending process based at least in part on the ingredients,the order of ingredients, and dietary goals. As described herein, theintelligent blending process may include an instruction or set ofinstructions for operation of a blender device.

What has been described above may be further understood with referenceto the following figures. FIGS. 11 and 12 provide exemplary operatingenvironments or systems capable of implementing one or more systems,apparatuses, or processes described above. FIGS. 11 and 12 are notintended to limit the scope of such systems, apparatuses, or processes.By way of example, computing environment 1100 may refer to one or moreembodiment of the various embodiments described with reference to theabove figures. However, variations to computing environment 1100 may beobvious to achieve aspects or processes described herein.

FIG. 11 is a schematic diagram of a computing environment 1100 inaccordance with various disclosed aspects. It is noted that environment1100 may include various other components or aspects. As depicted,system 1100 may include one or more client(s) 1102, one or moreserver(s) 1104, one or more client data store(s) 1120, one or moreserver data store(s) 1110, and a communication framework 1106.

While depicted as a desktop computer(s), client(s) 1102 may includevarious other devices that may comprise hardware and/or software (e.g.,program threads, processes, computer processors, non-transitory memorydevices, etc.). In an example, client(s) 1102 may include laptopcomputers, smart phones, tablet computers, blender devices, wearables,etc.). The client(s) 1102 may include or employ various aspectsdisclosed herein. For example, client(s) 1102 may include or employ allor part of various systems (100, 200, 300, etc.) and processes (e.g.,method 700, 800, 900, etc.) disclosed herein.

Likewise, server(s) 1104 may include various devices that may comprisehardware and/or software (e.g., program threads, processes, computerprocessors, non-transitory memory devices, etc.). Server(s) 1104 mayinclude or employ various aspects disclosed herein. For example,server(s) 1104 may include or employ all or part of various systems(100, 200, 300, etc.) and processes (e.g., method 700, 800, 900, etc.)disclosed herein. It is noted that server(s) 1104 and client(s) 1102 maycommunicate via communication framework 1106. In an exemplarycommunication, client(s) 1102 and server(s) 1104 may utilize packeteddata (e.g., data packets) adapted to be transmitted between two or morecomputers. For instance, data packets may include coded informationassociated with blending processes, dietary information of ingredients,or the likes.

Communication framework 1106 may comprise various network devices (e.g.,access points, routers, base stations, etc.) that may facilitatecommunication between client(s) 1102 and server(s) 1104. It is notedvarious forms of communications may be utilized, such as wired (e.g.,optical fiber, twisted copper wire, etc.) and/or wireless (e.g.,cellular, Wi-Fi, near field communication, etc.) communications.

In various embodiments, client(s) 1102 and server(s) 1104 mayrespectively include or communicate with one or more client datastore(s) 1120 or one or more server data store(s) 1110. The data storesmay store data local to client(s) 1102 or server(s) 1104.

In at least one embodiment, a client of client(s) 1102 may transfer datadescribing a recipe, user account data, ratings, or the likes to aserver of server(s) 1104. The server may store the data and/or employprocesses to alter the data. For example, the server may transmit thedata to other clients of client(s) 1102.

FIG. 12 is a block diagram of a computer system 1200 that may beemployed to execute various disclosed embodiments. Its is noted thatvarious components may be implement in combination with computerexecutable instructions, hardware devices, and/or combinations ofhardware and software devices that may be performed by computer system1200.

Computer system 1200 may include various components, hardware devices,software, software in execution, and the likes. In embodiments, computersystem 1200 may include computer 1200. Computer 1200 may include asystem bus 1208 that couples various system components. Such componentsmay include a processing unit(s) 1204, system memory device(s) 1206,disk storage device(s) 1214, sensor(s) 1235, output adapter(s) 1234,interface port(s) 1230, and communication connection(s) 1244. One ormore of the various components may be employed to perform aspects orembodiments disclosed herein. In an aspect, the computer system 1200 may“learn,” such as described above user preferences based uponmodifications of recipes by users, through rating of recipes bothpositively and negatively. For example, the computer system 1200 maymodify a particular recipe (or a set thereof) as the majority of usersor supermajority thereof have disapproved of the recipe (such as fortaste, texture, consistency, temperature, or a variety of thesefactors). The computer system 1200 may dynamically push out the revisedrecipe or receive the revised recipe as applicable.

Processing unit(s) 1204 may comprise various hardware processingdevices, such as single core or multi-core processing devices. Moreover,processing unit(s) 1204 may refer to a “processor,” “controller,”“computing processing unit (CPU),” or the likes. Such terms generallyrelate to a hardware device. Additionally, processing unit(s) 1204 mayinclude an integrated circuit, an application specific integratedcircuit (ASIC), a digital signal processor (DSP), a field programmablegate array (FPGA), a programmable logic controller (PLC), a complexprogrammable logic device (CPLD), a discrete gate or transistor logic,discrete hardware components, or the likes.

System memory 1206 may include one or more types of memory, suchvolatile memory 1210 (e.g., random access memory (RAM)) and non-volatilememory 1212 (e.g., read-only memory (ROM)). ROM may include erasableprogrammable ROM (EPROM), electrically erasable programmable ROM(EEPROM). In various embodiments, processing unit(s) 1204 may executecomputer executable instructions stored in system memory 1206, such asoperating system instructions and the likes.

Computer 1202 may also one or more hard drive(s) 1214 (e.g., EIDE,SATA). While hard drive(s) 1214 are depicted as internal to computer1202, it is noted that hard drive(s) 1214 may be external and/or coupledto computer 1202 via remote connections. Moreover, input port(s) 1230may include interfaces for coupling to input device(s) 1228, such asdisk drives. Disk drives may include components configured to receive,read and/or write to various types of memory devices, such as magneticdisks, optical disks (e.g., compact disks and/or other optical media),flash memory, zip drives, magnetic tapes, and the likes.

It is noted that hard drive(s) 1214 and/or other disk drives (ornon-transitory memory devices in general) may store data and/orcomputer-executable instructions according to various describedembodiments. Such memory devices may also include computer-executableinstructions associated with various other programs or modules. Forinstance, hard drives(s) 1214 may include operating system modules,application program modules, and the likes. Moreover, aspects disclosedherein are not limited to a particular operating system, such as acommercially available operating system.

Input device(s) 1228 may also include various user interface devices orother input devices, such as sensors (e.g., microphones, pressuresensors, light sensors, etc.), scales, cameras, scanners, facsimilemachines, and the likes. A user interface device may generateinstructions associated with user commands. Such instructions may bereceived by computer 1202. Examples of such interface devices include akeyboard, mouse (e.g., pointing device), joystick, remote controller,gaming controller, touch screen, stylus, and the likes. Input port(s)1230 may provide connections for the input device(s) 1228, such as viauniversal serial ports USB ports), infrared (IR) sensors, serial ports,parallel ports, wireless connections, specialized ports, and the likes.

Output adapter(s) 1234 may include various devices and/or programs thatinterface with output device(s) 1236. Such output device(s) 1236 mayinclude LEDs, computer monitors, touch screens, televisions, projectors,audio devices, printing devices, or the likes.

In embodiments, computer 1202 may be utilized as a client and/or aserver device. As such, computer 1202 may include communicationconnection(s) 1244 for connecting to a communication framework 1242).Communication connection(s) 1244 may include devices or componentscapable of connecting to a network. For instance, communicationconnection(s) 1244 may include cellular antennas, wireless antennas,wired connections, and the likes. Such communication connection(s) 1244may connect to networks via communication framework 1242. The networksmay include wide area networks, local area networks, facility orenterprise wide networks (e.g., intranet), global networks (e.g.,Internet), satellite networks, and the likes. Some examples of wirelessnetworks include Wi-Fi, Wi-Fi direct, BLUETOOTH™, Zigbee, and other802.XX wireless technologies. It is noted that communication framework1242 may include multiple networks connected together. For instance, aWi-Fi network may be connected to a wired Ethernet network.

The terms “component,” “module,” “system,” “interface,” “platform,”“service,” “framework,” “connector,” “controller,” or the like aregenerally intended to refer to a computer-related entity. Such terms mayrefer to at least one of hardware, software, or software in execution.For example, a component may include a computer-process running on aprocessor, a processor, a device, a process, a computer thread, or thelikes. In another aspect, such terms may include both an applicationrunning on a processor and a processor. Moreover, such terms may belocalized to one computer and/or may be distributed across multiplecomputers.

What has been described above includes examples of the presentspecification. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the present specification, but one of ordinary skill in theart may recognize that many further combinations and permutations of thepresent specification are possible. Each of the components describedabove may be combined or added together in any permutation to define theblending system 100. Accordingly, the present specification is intendedto embrace all such alterations, modifications and variations that fallwithin the spirit and scope of the appended claims. Furthermore, to theextent that the term “includes” is used in either the detaileddescription or the claims, such term is intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim.

What is claimed is:
 1. A blender system comprising: a base housing amotor; a container selectively attached with the base; a blendingmechanism positioned in the container; a memory storing at least onerecipe; a processor, communicatively coupled to the memory thatgenerates a blending process comprising a sequence of motor speeds anddurations, wherein the processor generates the blending process at leastas a function of a set of ingredients of the at least one recipe andfacilitates execution of the blending process to operate the motor; anda wireless communication device communicatively coupled to the processorto at least receive input or modify the at least one recipe, wherein thewireless communication device further comprises a user interfaceoperatively receiving a rating identifying a user's affinity for ablended product and communicates the rating to a network, wherein theuser's affinity is stored in at least one of the memory, the wirelesscommunication device, or a remote memory communicatively coupled to thenetwork, wherein the processor receives user feedback related to atleast one of a flavor, a consistency, or appearance of a blendedproduct, and wherein the processor applies the user feedback, theblending process, the at least one recipe, and the user's affinity to anattribute vector to infer modifications to the at least one recipe, andwherein the processor further modifies the recipe based on the inferredmodification.
 2. The blender system of claim 1, wherein the blendingprocess is further generated as a function of user preference that isstored in the memory or received via the wireless communication deviceassociated with blending of the at least one recipe.
 3. The blendersystem of claim 1, wherein generating the blending process furthercomprises: selecting operating parameters associated with the motorbased at least in part on a characteristic of the set of ingredients,wherein the characteristic is stored in the memory or received via thewireless communication device.
 4. The blender system of claim 1, whereingenerating the blending process further comprises: generating theblending process as a function of a target consistency describing adesired thickness of blended ingredients resulting from blending of theset of ingredients, wherein the target consistency is stored in thememory or received via the wireless communication device.
 5. The blendersystem of claim 1, wherein the processor receives data describing aproperty of at least one ingredient of the set of ingredients via thememory or the wireless communication device.
 6. The blender system ofclaim 5, wherein the data comprises data of a cooked status, atemperature state, or a preparation status of the at least oneingredient prior to operation of the motor.
 7. The blender system ofclaim 5, wherein the data comprises a quantity of the at least oneingredient.
 8. The blender system of claim 7, further comprising ameasuring device communicatively coupled with the processor to determinethe quantity.
 9. The blender system of claim 8, wherein the measuringdevice includes a scale.
 10. The blender system of claim 1, furthercomprising an ingredient identification device communicatively coupledwith the processor configured to identify ingredients added to thecontainer.
 11. The blender system of claim 10, wherein the ingredientidentification device includes at least one of camera, optical scanningdevice, spectrometer, multi-wave length scanner, or electronic nose. 12.The blender system of claim 10, wherein the processor modifies the atleast one recipe based upon at least one ingredient identified by theingredient identification device.
 13. The blender system of claim 1,wherein the blending mechanism includes a blade assembly.
 14. Theblender system of claim 1, further comprising: a second base housing asecond motor; a second container selectively attached with the secondbase; a second blending mechanism positioned in the second container; asecond memory storing the at least one recipe; a second processor,communicatively coupled to the second memory that facilitates executionof at least one instruction to operate the second motor; and wherein thewireless communication device is communicatively coupled to the secondprocessor to modify the at least one recipe.
 15. The blender system ofclaim 1, wherein the communication device is a networked computer. 16.The blender system of claim 13, wherein the at least one recipe ismodified based upon consumer ratings stored in the memory or received bythe wireless communication device.
 17. The blender system of claim 14,wherein the second blending mechanism includes a blade assembly.
 18. Ablender system comprising: a base housing a motor; a containerselectively attached with the base; a blending mechanism positioned inthe container; a memory storing at least one recipe; an ingredientidentification device configured to capture information for identifyingingredients added to the container, wherein the ingredientidentification device includes at least one of a camera or an electronicnose; a processor disposed within the base housing and communicativelycoupled to the memory that generates a blending process comprising asequence of motor speeds and durations, and further coupled to theingredient identification device, wherein the processor identifies andanalyzes a set of ingredients of the at least one recipe added to thecontainer based at least in part on the information captured by theingredient identification device, generates the blending process atleast as a function of the set of ingredients of the at least onerecipe, and facilitates execution of the blending process by directlyand automatically operating the motor free of input from an interface;and a wireless communication device communicatively coupled to theprocessor to at least receive input or modify the at least one recipe.19. The blender system of claim 18, wherein the processor modifies theat least one recipe based upon user feedback related to at least one ofa flavor, a consistency, or appearance of a blended product.
 20. Theblender system of claim 18, wherein the blending process is furthergenerated as a function of user preference that is stored in the memoryor received via the wireless communication device associated withblending of the at least one recipe.
 21. The blender system of claim 18,wherein generating the blending process further comprises: selectingoperating parameters associated with the motor based at least in part ona characteristic of the set of ingredients, wherein the characteristicis stored in the memory or received via the wireless communicationdevice.
 22. The blender system of claim 18, wherein generating theblending process further comprises: generating the blending process as afunction of a target consistency describing a desired thickness ofblended ingredients resulting from blending of the set of ingredients,wherein the target consistency is stored in the memory or received viathe wireless communication device.
 23. The blender system of claim 18,wherein the processor receives data describing a property of at leastone ingredient of the set of ingredients via the memory or the wirelesscommunication device.
 24. The blender system of claim 23, wherein thedata comprises data of a cooked status, a temperature state, or apreparation status of the at least one ingredient prior to operation ofthe motor.
 25. The blender system of claim 23, wherein the datacomprises a quantity of the at least one ingredient.
 26. The blendersystem of claim 25, further comprising a measuring devicecommunicatively coupled with the processor to determine the quantity.27. The blender system of claim 26, wherein the measuring deviceincludes a scale.
 28. The blender system of claim 18, wherein theprocessor modifies the at least one recipe based upon at least oneingredient identified by the ingredient identification device.
 29. Theblender system of claim 18, wherein the blending mechanism includes ablade assembly.
 30. The blender system of claim 18, wherein thecommunication device is a networked computer.
 31. The blender system ofclaim 29, wherein the at least one recipe is modified based uponconsumer ratings stored in the memory or received by the wirelesscommunication device.